Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments

@article{Smyth2004LinearMA,
  title={Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments},
  author={Gordon K. Smyth},
  journal={Statistical Applications in Genetics and Molecular Biology},
  year={2004},
  volume={3},
  pages={1 - 25}
}
  • G. Smyth
  • Published 12 February 2004
  • Computer Science, Medicine
  • Statistical Applications in Genetics and Molecular Biology
The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples… Expand

Paper Mentions

Observational Clinical Trial
MicroRNAs are small molecules which have recently been discovered in cells. They are known to be responsible for the normal development of cells and when they are disrupted can… Expand
ConditionsGlioma, Neurofibromatosis Type 1
Observational Clinical Trial
Biomarkers are small molecules that can be detected in the body fluids of patients; they often correlate with the presence of a cancer. MicroRNAs and proteins are small molecules… Expand
ConditionsCentral Nervous System Tumor
Error-Pooling Empirical Bayes Model for Enhanced Statistical Discovery of Differential Expression in Microarray Data
  • HyungJun Cho, J. Lee
  • Computer Science
  • IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
  • 2008
TLDR
An empirical Bayes (EB) heterogeneous error model with error-pooling prior specifications for varying technical and biological errors in the microarray data is proposed, showing that HEM is statistically more powerful than SAM and ANOVA, particularly when the sample size is smaller than five. Expand
Hierarchical Bayes models for cDNA microarray gene expression.
TLDR
This paper presents two full hierarchical Bayes models for detecting gene expression, of which one (D) describes the microarray data very well and compares the full Bayes and empirical Bayes approaches with respect to model assumptions, false discovery rates and computer running time. Expand
Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments
TLDR
A Bayesian hierarchical normal model is used to define a novel Intensity-Based Moderated T-statistic (IBMT), which is completely data-dependent using empirical Bayes philosophy to estimate hyperparameters, and thus does not require specification of any free parameters. Expand
Laplace Approximated EM Microarray Analysis: An Empirical Bayes Approach for Comparative Microarray Experiments
A two-groups mixed-effects model for the comparison of (normalized) microarray data from two treatment groups is considered. Most competing parametric methods that have appeared in the literature areExpand
A flexible approximate likelihood ratio test for detecting differential expression in microarray data
TLDR
This work proposes a method based on Generalized Logistic Distribution of Type II (GLDII) to deal with possible violations of the normality assumption in analyzing gene expression data, and generalizes the two-class ALRT method to multi-class microarray data. Expand
A Laplace mixture model for identification of differential expression in microarray experiments.
TLDR
A Laplace mixture model is introduced as a long-tailed alternative to the normal distribution when identifying differentially expressed genes in microarray experiments, and an extension to asymmetric over- or underexpression is provided. Expand
Hierarchical Bayes variable selection and microarray experiments
Hierarchical and empirical Bayes approaches to inference are attractive for data arising from microarray gene expression studies because of their ability to borrow strength across genes in makingExpand
Flexible empirical Bayes models for differential gene expression
TLDR
This paper extends both the GG and LNN modeling frameworks to allow for gene-specific variances and proposes EM based algorithms for parameter estimation and shows that the two extended models significantly reduce the false positive rate while keeping a high sensitivity when compared to the originals. Expand
LARGE-SCALE SIMULTANEOUS INFERENCE WITH APPLICATIONS TO THE DETECTION OF DIFFERENTIAL EXPRESSION WITH MICROARRAY DATA
An important problem in microarray experiments is the detection of genes that are differentially expressed in agiven mumber of classes. We consider a straightforward and easily implemented method forExpand
Establishing Informative Prior for Gene Expression Variance from Public Databases
Identifying differential expressed genes across various conditions or genotypes is the most typical approach to studying the regulation of gene expression. An estimate of gene-specific variance isExpand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 42 REFERENCES
On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles.
TLDR
A general empirical Bayes modelling approach which allows for replicate expression profiles in multiple conditions is proposed and used in a study of mammary cancer in the rat, where four distinct patterns of expression are possible. Expand
Replicated microarray data
cDNA microarrays permit us to study the expression of thousands of genes simultaneously. They are now used in many different contexts to compare mRNA levels between two or more samples of cells.Expand
A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes
TLDR
A Bayesian probabilistic framework for microarray data analysis is developed that derives point estimates for both parameters and hyperparameters, and regularized expressions for the variance of each gene by combining the empirical variance with a local background variance associated with neighboring genes. Expand
Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models
TLDR
A statistical approach is presented that allows direct control over the percentage of false positives in such a list of differentially expressed genes and, under certain reasonable assumptions, improves on existing methods with respect to the percentages of false negatives. Expand
Analysis of Variance for Gene Expression Microarray Data
TLDR
It is demonstrated that ANOVA methods can be used to normalize microarray data and provide estimates of changes in gene expression that are corrected for potential confounding effects and establishes a framework for the general analysis and interpretation of micro array data. Expand
Variance stabilization applied to microarray data calibration and to the quantification of differential expression
TLDR
A statistical model for microarray gene expression data that comprises data calibration, the quantifying of differential expression, and the quantification of measurement error is introduced, and a difference statistic Deltah whose variance is approximately constant along the whole intensity range is derived. Expand
Empirical Bayes Analysis of a Microarray Experiment
Microarrays are a novel technology that facilitates the simultaneous measurement of thousands of gene expression levels. A typical microarray experiment can produce millions of data points, raisingExpand
Bayesian Models for Gene Expression With DNA Microarray Data
Two of the critical issues that arise when examining DNA microarray data are (1) determination of which genes best discriminate among the different types of tissue, and (2) characterization ofExpand
Resampling-based Multiple Testing for Microarray Data Analysis
The burgeoning field of genomics has revived interest in multiple testing procedures by raising new methodological and computational challenges. For example, microarray experiments generate largeExpand
Statistical issues in microarray data analysis.
TLDR
Any microarray experiment consists of several components: carrying out an appropriately designed (replicated) plant experiment; array processing, which includes several steps of data acquisition and normalization; and analysis of expression data to identify differentially expressed genes and overall patterns of expression. Expand
...
1
2
3
4
5
...